How Predictive Maintenance is Revolutionizing Fire Alarm and Control Systems

As you know, predictive maintenance (PdM) - the use of real-time data, analytics, and AI to forecast equipment failures - has transformed how we approach system reliability. As industrial control systems power everything from energy grids to transit networks, the ability to detect and address faults before they occur has become mission-critical.

The Association for Advancing Automation (A3) recently highlighted the rapid growth of PdM across sectors. Their analysis emphasizes not only the market momentum (currently expected to reach $18.5 billion by 2028), but also the accelerating pace of technological advancement.

Here, we'll examine why traditional maintenance models are no longer adequate, explore the enabling technologies behind PdM, and look at how certain monitoring platforms support this shift - especially in fire alarm and supervisory control systems.

Reactive and Scheduled Maintenance Leaves You Exposed

For decades, maintenance was mostly reactive ('fix it when it breaks') or scheduled ('replace it on a regular basis'). While these approaches worked reasonably well in simpler systems, they aren't enough for interconnected and/or data-rich environments.

Reactive maintenance can lead to unplanned downtime and emergency repair costs since you're left dealing with an issue after it has already made a noticeable impact. Scheduled maintenance, on the other hand, can waste time and resources replacing equipment that's still performing well.

The flaw in both models lies in a lack of real-time awareness. They don't account for how a system is actually performing in the moment. This leaves you vulnerable to undetected anomalies that can spiral into outages or safety hazards.

When you're operating critical infrastructure - whether that's a fire suppression system, public transit signal network, or power distribution node - failures aren't just inconvenient. They're dangerous and costly.

Predictive Maintenance Offers Prevention Backed by Data

Predictive maintenance uses sensors, software, and analytics to track performance trends and identify risks before they cause failures. A PdM-enabled system can analyze:

• Temperature, vibration, and voltage patterns
• Communication failures or latency
• Equipment usage trends over time
• Anomalies or subtle performance drifts

These systems continuously monitor asset health and use algorithms to detect when something's off - typically long before a failure would be apparent to human operators.

When applied to control or monitoring systems, PdM moves facilities from guesswork to proactive risk management.

The payoff is fewer breakdowns, lower maintenance costs, and more uptime. According to A3, PdM can reduce equipment breakdowns by up to 70% and cut maintenance costs by 30%. Those numbers translate directly to extended system life and stronger budget control.

4 Core Technologies Behind PdM

1. Artificial Intelligence and Machine Learning (AI/ML)

At the heart of predictive maintenance is the ability to learn and adapt. AI and ML systems ingest massive datasets and build models of 'normal' operation.

When those systems detect a deviation, they flag it for review or automatically trigger alerts. For example, a monitoring platform might log an unusual pattern in signal frequency or voltage fluctuation from a connected legacy FACP (Fire Alarm Control Panel).

While the alarm system appears to be functioning normally, this irregularity could indicate early-stage corrosion in terminal blocks or intermittent faults in relay contacts. These issues could lead to a future failure if left unchecked.

This precise detection allows maintenance teams to intervene strategically instead of relying on guesswork. Rather than replace every part on a schedule, they replace the right part at the right time.

2. Industrial Internet of Things (IIoT)

None of this insight would be possible without the data collected by IIoT-enabled monitoring devices. In the context of fire alarm systems, these sensors are embedded throughout control panels, annunciators, and interfacing equipment to measure:

• Line voltage integrity to remote panels
• Signal frequency from supervised inputs
• Battery health and backup power load
• Communication path status across IP, radio, or copper
• Environmental conditions in equipment enclosures (e.g., humidity, temperature)

With systems like Prism LX, these IIoT capabilities provide real-time diagnostics and trend analysis across distributed fire alarm networks. According to industry estimates, proactive fault detection via IIoT can reduce false alarms and communication failures by up to 30%.

More importantly, it supports a smarter facility where status changes and potential faults are reported automatically. This means that there's no manual inspection required.

3. Edge and Cloud Computing

Data from fire alarm monitoring points - especially in larger environments like military bases, transit facilities, or municipal buildings - must be processed both quickly and securely. Edge and cloud computing help make that happen.

Edge devices like on-site collectors or polling stations handle real-time signal processing and alarm verification. Meanwhile, cloud-based or centralized platforms can store historical alarm data, generate performance analytics, and support system-wide visibility across multiple buildings or jurisdictions.

The result is immediate responses to alarm conditions and degraded signals, combined with long-term data that informs preventative maintenance, regulatory compliance, and capital planning.

4. Digital Twins

Digital twins are real-time digital replicas of physical assets. They simulate behavior under different conditions - such as voltage drops, panel disconnects, or network congestion. This allows teams to test hypotheses without putting real assets at risk.

With predictive maintenance, digital twins help validate AI findings, model fault impact, and even train new staff on how to respond to emerging system conditions.

What a Predictive Maintenance Workflow Looks Like

Here's a simplified breakdown of how PdM typically works in a remote control context:

1. Data collection: Monitoring devices gather data from supervised input circuits, FACP communication lines, battery voltage, relay outputs, and environmental sensors within remote enclosures.
2. Data transmission: This data is transmitted via secure communication paths (radio, IP, copper, or fiber) to centralized monitoring equipment like the Prism LX.
3. Pattern analysis: Models assess the data and detect anomalies or deviations.
4. Alert generation: The system triggers an alert when patterns suggest a likely fault.
5. Proactive maintenance: Technicians receive actionable information and respond before a failure disrupts operations.

This type of workflow keeps systems running longer, reduces emergency maintenance, and increases transparency across teams and departments.

Translating PdM to Fire Alarm and Life Safety Monitoring

Most of the focus on PdM will center on equipment health (things like motors, pumps, or HVAC systems). But when you apply the same logic to fire alarm and supervisory systems, the stakes grow.

A mechanical failure might cost you hours of downtime. A fire detection failure could cost lives.

That's why facilities - especially schools, airports, government buildings, and military bases - are starting to incorporate predictive strategies into their safety infrastructure. This is where Prism LX becomes critical.

Digitize Supports Predictive Fire Monitoring

Digitize's Prism LX head-end system was built for environments that demand real-time awareness. While not a predictive AI platform architecturally, Prism LX provides many of the functional tools that allow teams to anticipate problems before they escalate:

Real-Time Status and Fault Visibility

Whether it's a pulled station, disconnected wire, or suppressed signal, Prism LX gives facilities a clear, immediate view into their alarm conditions. But more importantly, it also logs trends.

Are you seeing repeat trouble states from the same panel? Is one zone showing intermittent signal loss? These patterns are tracked and time-stamped, giving technicians a better sense of what may need attention.

Integration with Legacy Systems

Prism LX doesn't require you to rip and replace old infrastructure. It integrates with legacy fire panels via Digitize's Data Gathering Modules (such as the Muxpad II). The devices work together to convert hardwired alarm signals into a digital format compatible with modern protocols.

This enables predictions without requiring a total system overhaul.

Custom Logic for Early Alerts

Through its programmable logic functions, Prism LX can be configured to trigger alerts for defined pre-failure conditions. That could include:

• Multiple "Trouble" states in the same 24-hour window
• Excessive resets in a short time frame
• Repeated supervisory tamper detections

These alerts allow facilities to take action proactively - investigating and correcting the root cause before a more serious event occurs.

Networked Monitoring for Campus-Wide Coverage

Whether you're managing a school district, a transit depot, or a military installation, fire alarm systems often span multiple buildings or zones. Prism LX supports centralized, multi-zone monitoring from a single interface, with mapped layouts, color-coded statuses, and detailed reporting.

That means your team doesn't just know that a fault occurred - they know where, when, how often, and why it matters.

This is a diagram of a fire alarm system that takes advantage of both legacy and new fire alarm system components, all reporting to a Digitize System 3505 Prism LX head end unit. As you can see in the diagram, the Prism LX is able to interpret a wide range of signals.

It's Time to Think Predictively - Even in Public Infrastructure

Let's say you're responsible for safety systems at a regional transit center. Your fire alarms were installed a decade ago. You're compliant with code, but in recent months, one panel keeps showing line fault errors. It resets itself each time, so no one thinks much of it.

Then, during a holiday weekend, a fire starts in the electrical room. The panel fails to activate the alarms. The building is evacuated late. Damage and downtime follow.

This isn't just a failure of equipment - it's a failure to predict that failure.

Digitize's monitoring systems close that gap. They give your team - and local inspectors - early visibility into patterns that can signal risk. They provide documentation, trends, and alerts that reduce the likelihood of blind spots and system neglect.

And given that they're designed for interoperability, you can retrofit aging infrastructure and gain modern capabilities without massive cost or construction.

Digitize Makes Predictive Strategies Accessible

Digitize systems bring predictive-style monitoring to sectors that are too often left behind by industrial innovation.

No matter your industry or title, Digitize helps you implement monitoring systems that act like predictive tools. These help you with spotting risks, flagging repeat issues, and integrating with your larger safety protocols.

Prepare Today for the Emergencies of Tomorrow

Predictive maintenance isn't just a buzzword for factories and warehouses. It's a foundational shift in how we manage risk, reduce downtime, and uphold public safety.

By applying PdM principles to your fire alarm and supervisory infrastructure, you're not just checking code boxes. You're making your facility smarter, safer, and more responsive.

Digitize is ready to help you bring these strategies into your environment - on your terms, with your existing equipment, and with the confidence that your monitoring system will do more than react.

Contact us today at (973) 663-1011 or email info@digitize-inc.com to learn how the Prism LX and other Digitize solutions can help you protect your infrastructure with predictive awareness, modern integration, and uninterrupted safety coverage.

Andrew Erickson

Andrew Erickson is an Application Engineer at DPS Telecom, a manufacturer of semi-custom remote alarm monitoring systems based in Fresno, California. Andrew brings more than 18 years of experience building site monitoring solutions, developing intuitive user interfaces and documentation, and...Read More